Abstract

With the evolution of technology and the major role that technology now plays in the diagnosis and identification of disorders and difficulties, improving the accuracy of diagnostic systems is paramount. Improving and evaluating the way in which patterns of results are identified and classified may help uncover answers that are not always obvious. This paper attempts to discover such patterns found in brainwave signals in adults who have been diagnosed with dyslexia using classifiers. Electroencephalogram (EEG) signals captured during real-word and nonsense-word reading activities from adults with dyslexia were compared with normal controls. The classification was performed using Linear Support Vector Machine (LSVM) and Cubic Support Vector Machine (CSVM) on different lobes of the brain. The study revealed that the nonsense-words classifiers produced higher validation accuracies compared to real-words classifiers, confirming difficulties in phonological decoding skills seen in individuals with dyslexia are reflected in the brainwave patterns.